Workload, generic and work–family specific social supports and job stress
Bibliographic record
Abstract
Purpose The present study aims to investigate the mediating role of work–family conflict (WFC) and family–work conflict (FWC) on the effects of workload and the generic and specific work–family social support in job stress. Design/methodology/approach Using AMOS 20 through bootstrap analysis for indirect effect, the study assessed the abovementioned relationships based on data collected from 258 respondents in the hospitality industry in Quebec. Findings The findings indicate that workload increases job stress via WFC and FWC. Both generic and specific work–family social support decrease job stress through WFC and FWC. Organizational support for reconciling work and family life is more significant than generic supervisor support. Family support reduces job stress via WFC but not via FWC. Research limitations/implications In future studies, it would be interesting to explore the effects of variables such as gender, marital status, hotel category and the job category, as well as cultural origin. Practical implications The results of this research should alert employers in the hospitality industry to engage in family-friendly policies that include not only practices such as working time arrangements, family leave and onsite child care services, but also to be committed to create a family-friendly culture and to adopt the best forms of supportive policies at work. Originality/value By emphasizing cross-domain effects, the present research contributes to the existing knowledge by testing the mediating role of WFC and FWC in the effects of workload and various resources of social support on job stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".